Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Understanding mechanisms by which a disease acts can be important when prescribing a treatment regimen for a patient having such a disease. For some diseases, the current state of knowledge may not be at a level that allows for such a treatment regimen to be developed. Thus, methods of improving the level of understanding of disease mechanisms, or of screening for effective treatments even while remaining relatively unknowledgeable about a given disease mechanism, could be useful in treating patients.
Further, machine learning is a field in computing that involves a computing device training a model using “training data.” There are two primary classifications of methods of training models: supervised learning and unsupervised learning. In supervised learning, the training data is classified into data types, and the model is trained to look for variations/similarities among known classifications. In unsupervised learning, the model is trained using training data that is unclassified. Thus, in unsupervised learning, the model is trained to identify similarities based on unlabeled training data.
Once the model has been trained on the training data, the model can then be used to analyze new data (sometimes called “test data”). Based on the model's training, a computing device can use the trained model to evaluate the similarity of the test data.
There are numerous types of machine-learned models, each having its own set of advantages and disadvantages. One popular machine-learned model is an artificial neural network. The artificial neural network involves layers of structure, each trained to identify certain features of an input (e.g., an input image, an input sound file, or an input text file). Each layer may be built upon sub-layers that are trained to identify sub-features of a given feature. For example, an artificial neural network may identify composite objects within an image based on sub-features such as edges or textures.
Given the current state of computing power, in some artificial neural networks many such sub-layers can be established during training of a model. Artificial neural networks that include multiple sub-layers are sometimes referred to as “deep neural networks.” In some deep neural networks, there may be hidden layers and/or hidden sub-layers that identify composites or superpositions of inputs. Such composites or superpositions may not be human-interpretable.